Prevention over detection
For detect-and-respond vendors, the alert is the business model — they profit from the chase and the cleanup. We didn't extend the old model. We replaced it: neutralize threats before they breach.
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We replaced the detection model. We did not extend it.
We didn't add AI — we're made of it. But not the AI you're thinking of: no GenAI, no LLM, no SLM, no MCP gateway, and no dependency on them whatsoever. No model bolted onto a legacy scanner, no agentic framework wrapped around someone else's language model. Our architecture is neuromorphic — engineered to defend the AI era, native, not retrofitted.
Comprehension over signatures
Neuromorphic mathematics reads the intent inside an AI interaction — catching novel techniques that carry no prior signature. Detection tools record what happened; we anticipate what's about to.
No kernel agent by design
The userspace System Sensor — not a kernel agent — running in the user's session with native OS APIs, no kernel extension, driver, EDR hook, or root, paired with an AI-aware extension in the browser. Coverage at the point of interaction, across both data planes — with no dependency on GenAI, LLMs, SLMs, or MCP gateways, and zero token cost. Flat-cost by design.
The difference
Eight questions.
Five architectures. One of them acts.
Ask any AI security product the same eight questions a security architect asks in an evaluation. The answers diverge at the first one — where the control actually sits — and never converge again.
| Salience Cyber | Model-judged AI security | Post-collection analytics | The stack you already own | Policy only | |
|---|---|---|---|---|---|
| Where it sits | In-line, at the point of interaction | In the request path, as a proxy or gateway | On a server, after the fact | On the host and in the log, after execution | Nowhere. It is a document |
| What it reads | Intent | A model's reading of the text | Logs | Artifacts and behaviour | Nothing |
| Can the decider itself be talked to? | No language in the decision path to address | It reads attacker-controlled text by design | Not in the path; it does not decide | Not in the path; it decides after execution | The policy cannot. The person can |
| Zero-signature attacks | Anticipated | Bounded by what the model recognises | After the fact, on a copy | Stops what it has seen before | Unaddressed |
| Decision path | Microseconds — no model round-trip, decided in-process | A model round-trip, per interaction | Minutes to hours | After execution | A person, if someone reports it |
| What it costs | Flat. No per-token metering | Metered per token or per call | Per GB stored and queried | Per endpoint, plus retention | Nothing to license. Everything to enforce |
| What it adds to your attack surface | Two userspace sensors. No kernel driver, and no model in the decision path | A model in the decision path, with that model's exposure and update cycle | A second copy of your data | A kernel driver or an agent | Nothing technical |
| What you are left with | The hostile interaction is neutralized before it executes | A verdict from a model, and an alert | A report | A ticket | An assumption that people complied |
The words are the same on every website. The architecture is not.
What design partners are saying
Your employees are using AI tools you can’t see. Salience Cyber fixes that. It captures every AI interaction on the endpoint and turns it into one clean audit trail — visibility and enforcement in the same product, without an LLM sitting in the critical path or another EDR integration to maintain.